Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Dose Response Curve: Conventional Versus Nonmonotonic01:21

Dose Response Curve: Conventional Versus Nonmonotonic

The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...
P-value01:10

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spore germination response to capsule size and smoke: co-expression of bet-hedging and best-bet strategies in peatland mosses.

Plant biology (Stuttgart, Germany)·2025
Same author

'Pseudo-curare clefts' secondary to an aneurysmal right pulmonary artery.

Anaesthesia reports·2024
Same author

COVID-19 Stroke Apical Lung Examination Study 2: a national prospective CTA biomarker study of the lung apices, in patients presenting with suspected acute stroke (COVID SALES 2).

NeuroImage. Clinical·2024
Same author

The Effect of Surgical Weight Loss on Cognition in Individuals with Class II/III Obesity.

The journal of nutrition, health & aging·2023
Same author

Potential drug-drug interactions among hospitalised TB patients.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2022
Same author

Nonlocal Reaction-Diffusion Equations in Biomedical Applications.

Acta biotheoretica·2022
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: May 17, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Threshold estimation based on a p-value framework in dose-response and regression settings.

A Mallik1, B Sen, M Banerjee

  • 1Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A., atulm@umich.edu.

Biometrika
|October 11, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using p-values to detect threshold changes in regression functions. This approach is valuable for dose-response analyses in toxicology, pharmacology, and environmental statistics, offering a computationally simple solution.

More Related Videos

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine
08:33

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Published on: July 24, 2018

Related Experiment Videos

Last Updated: May 17, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine
08:33

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Published on: July 24, 2018

Area of Science:

  • Statistics
  • Biostatistics
  • Environmental Statistics

Background:

  • Identifying threshold levels where regression functions deviate from baseline is crucial in various scientific fields.
  • Applications include toxicological and pharmacological dose-response studies and environmental data analysis.

Purpose of the Study:

  • To develop a statistically sound method for estimating the threshold at which a regression function changes from its baseline.
  • To address this problem in two distinct sampling settings: multiple responses per covariate and standard regression settings.

Main Methods:

  • The procedure involves testing the hypothesis that the regression function is at its baseline at each covariate value.
  • Observed p-values are used to fit a piecewise constant function (stump) to estimate the threshold, exploiting distinct p-value behavior around the threshold.
  • The method's consistency and finite sample properties are evaluated through simulations.

Main Results:

  • The proposed threshold estimation method is shown to be consistent.
  • Simulations demonstrate the finite sample properties of the estimation procedure.
  • The approach is computationally simple and adaptable to various extensions.

Conclusions:

  • The developed method provides a reliable and efficient way to estimate threshold levels in regression functions.
  • The approach is versatile, extending to baseline value estimation, heteroscedastic errors, and time series analysis.
  • The method's practical utility is demonstrated through real-world data applications.